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Kernel Partial Least Squares for Stationary Data

Marco Singer, Tatyana Krivobokova, Axel Munk; 18(123):1−41, 2017.

Abstract

We consider the kernel partial least squares algorithm for non- parametric regression with stationary dependent data. Probabilistic convergence rates of the kernel partial least squares estimator to the true regression function are established under a source and an effective dimensionality condition. It is shown both theoretically and in simulations that long range dependence results in slower convergence rates. A protein dynamics example shows high predictive power of kernel partial least squares.

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© JMLR 2017.